Autonomous Infrastructure and Robotic Science Lead

The Computing, Environment, and Life Sciences (CELS) Directorate seeks an outstanding scientist to lead and support frontier research at the intersection of AI, autonomous platforms, data infrastructure, and domain science. The candidate will have established expertise across automated and autonomous experimental platforms and AI in addition to leadership of multi-disciplinary research programs and the development of novel research concepts.

The scientist will lead Argonne’s Rapid Prototyping Laboratory (RPL), a team of computer scientists, roboticists, data scientists, and subject matter experts, who develop hardware and software infrastructure for laboratory autonomy, support autonomous laboratories in domains including chemistry, biology, and quantum science, work with domain scientists to execute autonomous experiments, and advance laboratory autonomy and robotics.

RPL develops the open-source Modular Autonomous Discovery for Science (MADSci) software framework for the orchestration of autonomous laboratories in addition to software infrastructure supporting the operation, training, and execution of robotic workflows. The scientist would be responsible for directing activities towards the advancement of these internal capabilities in addition to the support and development of collaborations across Argonne and beyond.

Focus Areas (expertise in one or more is highly desirable):

• Autonomous laboratories for chemistry, materials, biology, etc.
• AI/ML for predictive modeling and inverse design
• Generative models, reinforcement learning, and agent-based approaches to streamline experimentation and accelerate discovery
• Integration of HPC, data infrastructure, and ML pipelines for >

  Position Requirements

  • Completed Ph.D. in Computer Science, Materials Science, Physics, Chemistry, or a related field, and a minimum of 4+ years of related experience
  • Proven research track record in deploying automated and autonomous platforms and AI/ML towards accelerating science
  • Demonstrated ability to formulate scientific problems relevant to the DOE portfolio
  • Strong oral and written communication skills, with the ability to work effectively with internal and external collaborators to achieve established goals
  • Demonstrated ability to collaborate in a multidisciplinary environment and provide scientific guidance to a diverse research community
  • Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork

  Application Instructions:

  Submit the following materials as attachments to your application:

  • Cover letter detailing how your experience and expertise align with and will contribute to this position
  • Curriculum vitae with publication list
  • 1-page research statement outlining proposed research directions

  About Argonne and the Rapid Prototyping Lab

  
  Argonne National Laboratory is a U.S. Department of Energy multidisciplinary science and engineering research center, operated by UChicago Argonne, LLC. Argonne tackles the largest scientific and engineering challenges of our time, from clean energy and advanced materials to artificial intelligence and quantum information science.

  The Rapid Prototyping Lab (RPL), in the Data Science and Learning division, develops integrated hardware and software solutions to accelerate scientific discovery through robotics and AI. RPL serves as a software and robotics hub where scientists collaborate, train the next-generation autonomous-discovery workforce, and develop open-source infrastructure for self-driving labs. RPL projects span new materials for energy storage, discovery of antimicrobial compounds, isotope production for medical applications, and more.

  MADSci is RPL's flagship open-source software ecosystem and a core enabling technology for Argonne's broader Autonomous Discovery initiative, which aims to transform laboratory science by combining robotics, AI, and simulation to design, execute, and learn from experiments at unprecedented scale.

  For more information:

  • Rapid Prototyping Lab:https://rpl.cels.anl.gov/
  • Autonomous Discovery at Argonne:https://www.anl.gov/autonomous-discovery
  • MADSci on GitHub:https://github.com/AD-SDL/MADSci
  • AD-SDL organization on GitHub:https://github.com/AD-SDL

  Job Family

  Research Development (RD)

  Job Profile

  Computational Science 3

  Worker Type

  Regular

  Time Type

  Full time
  The expected hiring range for this position is $116,250.00 - $181,350.00.

  Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

  Click here to view Argonne employee benefits!

  As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.

  Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department.

  All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.